# -*- coding: utf-8 -*-
# CNN 卷积神经网络

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

# def add_layer(inputs,in_size,out_size,activation_function=None):
#     Weights = tf.Variable(tf.random_normal([in_size,out_size]))
#     biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)
#     Wx_plus_b = tf.matmul(inputs,Weights) + biases
#
#     if activation_function is None :
#         outputs = Wx_plus_b
#     else:
#         outputs = activation_function(Wx_plus_b)
#
#     return outputs

def compute_accuracy(v_xs,v_ys):
    global prediction
    # y_pre = sess.run(prediction,feed_dict={xs:v_xs,keep_prob:1})        # 预测值
    correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(v_ys,1))     # 比较预测值和实际值是否相等
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})
    return result

def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev=.1)          # 随机的值？
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(.1,shape=shape)                   # 0.1
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')          # strides 抽取像素的步长为1

def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')



# 定义输入数据placeholder
xs = tf.placeholder(tf.float32, [None,784])      # 不指定训练集图片个数，每个数据图片有28*28 = 784个点
ys = tf.placeholder(tf.float32, [None,10])
keep_prob = tf.placeholder(tf.float32)           # 防止过拟合？
x_image = tf.reshape(xs,[-1,28,28,1])
# print(x_image.shape) # [n_samples,28,28,1]

## conv1 layer
W_conv1 = weight_variable([5,5,1,32])  # patch 5x5 ,in size 1,out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)     # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)                             # output size 14x14x32

## conv2 layer
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)       # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)                             # output size 7x7x64

# # func1 layer
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])              # 改变形状,扁平化处理 将[n_samples,7,7,64] 转换为[n_samples,7*7*64]
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

# # func2 layer
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)


# 定义输出layer
# prediction = add_layer(xs,784,10, activation_function=tf.nn.softmax)


# prediction与正确值的误差
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))     # 决策树算法?

# train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(5000):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    # print(batch_xs)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))


